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도킨스(Richard Dawkins)는 자신의 저서 󰡔이기적 유전자󰡕와 󰡔확장된 표현형󰡕을 통해 생물의 진화가 개체나 집단이 아닌 유전자 수준에서 일어난다고 주장하였다. 유전자 선택설로 명명되는 이 이론에 따르면 개체의 형질과 행동은 유전자 복제와 확산을 최대화하는 방향으로 선택되며, 이때 생물학적 개체는 유전자를 운반하기 위한 수단이자 유전자에 복종하는 수동적인 기계와 같다. 이는 결국 동식물의 다양한 표현형질뿐만 아니라 인간의 사고, 믿음, 행동방식에 이르는 광범위한 현상을 유전자간 경쟁과 선택이라는 단일한 요소로 설명하려는 시도로 해석되어 생물학적 결정론이라는 비판에 부딪혔다. 특히 도킨스가 이론을 설명하는 과정에 극단적 은유를 사용한 점, 확장된 표현형이라는 개념을 통해 유전자의 결정력을 강화한 점, 획득형질의 유전과 니치구성의 가능성을 부정함으로서 유전에 환경적 요인이 개입할 여지를 차단한 점 등이 비판의 주요 근거로 인용되었다. 하지만 도킨스는 자신의 저서와 논문을 통해 이러한 비판이 잘못되었다고 반박한다. 그가 ‘운반기계’나 ‘로봇’과 같은 표현을 통해 유전자에 대해 개체가 갖는 수동성을 설명하는데 초점을 맞추다보니 개체의 다른 특성을 무시하거나 왜곡하는 것으로 오해되었지만, 이는 과학적 은유가 갖는 본질적 한계 때문이지 자신이 의도한 것은 아니라고 주장한다. 또한 유전자의 명령에 복종하는 수동성외에, 밈의 학습을 통해 유전자의 명령에 저항하는 개체의 능동성에 주목하였으며 기존 이론에서 주장하는 느슨한 의미의 획득형질의 유전가능성에 반대하지도 않는다고 주장한다. 다만 느슨한 기준을 만족시키는 경우에 근거해 획득형질의 유전 가능성을 주장하는 것은 지지받기 어려우며 이를 증명하기 위해서는 보다 엄격한 기준을 만족시키는 사례가 필요하다고 역설한다. 이에 본 연구는 도킨스의 이러한 반박을 토대로 유전자선택설에 대한 비판이 도킨스의 의도가 무시된 상황에서 일방적으로 진행되었다는 점을 지적하고, 유전자선택설의 내용과 그것이 지니는 철학적 함축, 도킨스의 관점이 유전자결정론으로 비판받게 된 과정, 이에 대한 도킨스의 반박 등을 분석하였다. 나아가 도킨스의 이론을 둘러싼 논쟁이 소모적으로 전개된 이유를 진단하고, 대안 책으로서 생산적 논의에 필요한 태도와 관점을 제안하였다.

Richard Dawkins claims in his books, The Selfish Gene and The Extended Phenotype that the evolution of life develops not in the individual level or group level but in the genetic level. According to the theory as referred to 'Genetic Selection Principle', the character and behavior of individual is selected to maximize the copy and proliferation of genes, and individuals work as vehicles to move genes and so they are ike passive machine obeying the order of genes. This is criticized as a biological determinism because it is to be understood as an attempt to explain for widespread phenomenon such as various representation of men's thoughts, beliefs as well as all plant and animal traits with the only factor, competition and selection between genes. In particular, Dawkins' using extreme metaphors, trying to strengthen the impact of a gene with the notion of ‘The Extended Phenotype’, nullifying the contribution of environmental factors in the genetic phenomenon and denying the possibility of niche construction and inheritance of acquired characters have been quoted as reasons why he was criticised. However Dawkins has argued in his own books and articles that the he has been misunderstood to ignore or distort the various phenotype in the level of individual and it is caused by the metaphor such as 'gene carrying machines' or 'robots' that he uses to focus on the passivity of individual. That is not what he intends but an aspect of essential limitation of scientific metaphor. He recognised that genes also have the activeness of resisting to the orders of gene by learning 'memes'. And he argues that he does not oppose to the inheritance of acquired characters. But he keeps the opinion that it is to supported only on the basis of loose definitions, not with his own one which is stricter than the other. He can not have found the case to support his strict criteria. As a result, this study is performed to point out that criticism to Dawkins' theory has been suggested unilaterally ignoring the intention and meaning of his theory and analyze what his theory includes and implicates. Besides it aims to clarify the reason why and the way how his theory is categorized and criticized as genetic determinism and analysize what he addresses to refute it. On the top of it, reviewing the theoretical meaning and philosophical implication of genetic selection, this study will diagnose the reason why the argument about Dawkins theory was not productive and will suggest the appropriate attitude and perspective to solve it.

The specific genetic modification in porcine somatic cells by gene targeting has been very difficult because of low efficiency of homologous recombination. To improve gene targeting, we designed three kinds of knock-out vectors with α1,3-galactosyltransferase gene (α1,3-GT gene), DT-A/pGT5’/neo/pGT3’, DT- A/NLS/pGT5’/neo/pGT3’ and pGT5’/neo/ pGT3’/NLS. The knock-out vectors consisted of a 4.8-kb fragment as the 5’ recombination arm (pGT5’) and a 1.9-kb fragment as the 3’ recombination arm (pGT3’). We used the neomycin resistance gene (neo) as a positive selectable marker and the diphtheria toxin A (DT-A) gene as a negative selectable marker. These vectors have a neo gene insertion in exon 9 for inactivation of α1,3-GT locus. DT-A/pGT5’/neo/pGT3’ vector contain only positive-negative selection marker with conventional targeting vector. DT-A/NLS/pGT5’/neo/pGT3’ vector contain positive-negative selection marker and NLS sequences in upstream of 5’ recombination arm which enhances nuclear transport of foreign DNA into bovine somatic cells. pGT5’/neo/pGT3’/NLS vector contain only positive selection marker and NLS sequence in downstream of 3’ recombination arm, not contain negative selectable marker. For transfection, linearzed vectors were introduced into porcine ear fibroblasts by electroporation. After 48 hours, the transfected cells were selected with 300 μg/ml G418 during 12 day. The G418-resistant colonies were picked, of which 5 colonies were positive for α 1,3-GT gene disruption in 3´ PCR and southern blot screening. Three knock-out somatic cells were obtained from DT-A/NLS/ pGT5’/neo/pGT3’ knock-out vector. Thus, these data indicate that gene targeting vector using nuclear localization signal and negative selection marker improve targeting efficiency in porcine somatic cells.

The ovary undergoes substantial physiological changes along with estrus phase to mediate negative/positive feedback to the upstream reproductive tissues and to play a role in producing a fertilizable oocyte in the developing follicles. However, the disorder of estrus cycle in female can lead to diseases, such as cystic ovary which is directly associated with decline of overall reproductive performance. In gene expression studies of ovaries, quantitative reverse transcription polymerase chain reaction (qPCR) assay has been widely applied. During this assay, although normalization of target genes against reference genes (RGs) has been indispensably conducted, the expression of RGs is also variable in each experimental condition which can result in false conclusion. Because the understanding for stable RG in porcine ovaries was still limited, we attempted to assess the stability of RGs from the pool of ten commonly used RGs (18S, B2M, PPIA, RPL4, SDHA, ACTB, GAPDH, HPRT1, YWHAZ, and TBP) in the porcine ovaries under different estrus phase (follicular and luteal phase) and cystic condition, using stable RG-finding programs (geNorm, Normfinder, and BestKeeper). The significant (p < 0.01) differences in Ct values of RGs in the porcine ovaries under different conditions were identified. In assessing the stability of RGs, three programs comprehensively agreed that TBP and YWHAZ were suitable RGs to study porcine ovaries under different conditions but ACTB and GAPDH were inappropriate RGs in this experimental condition. We hope that these results contribute to plan the experiment design in the field of reproductive physiology in pigs as reference data.

An extremely crucial step in the diagnosis of cancers is to select a small number of informative genes for accurate classification. This issue has become a hot focus in the data mining of gene expression profiles. Especially for data with a large number of cancer types, many conventional classification methods show very poor performance. Here, we proposed a new approach for gene selection and multi-cancer classification based on step-by-step improvement of classification performance (SSiCP). The SSiCP gene selection algorithms were evaluated over the NCI60 and GCM benchmark datasets, with accuracy of 96.6% and 95.5% in 10-fold cross-validation, respectively. Furthermore, the SSiCP outperformed recently published algorithms when applied to another two multi-cancer data sets. Computational evidence indicated that SSiCP can avoid overfitting effectively. Compared with various gene selection algorithms, the implementation of SSiCP is simple and many of the selected genes by SSiCP are shown to be closely related to cancers.

DNA microarray technique can detect tens of thousands of genes activity in cells and has been widely used in clinical diagnosis. However, microarray data has characteristics of high dimension and small samples, moreover many irrelevant and redundant genes also decrease performance of classification algorithm .Mutual information is very effective method and has widely been used in feature gene selection, but it cannot directly deal with continuous features. Therefore, this paper proposes a novel feature gene selection method to resolve this problem. Firstly, a lot of irrelevant genes are eliminated from original data by using reliefF algorithm , and the candidate subset of genes is obtained; Secondly, a algorithm based on neighborhood mutual information and forward greedy search strategy which deals with directly continuous features is proposed to select feature genes in above genes subset. Here, because radius of neighborhood greatly affects reduction performance, differential evolution algorithm is applied to optimize radius before reduction. The simulation results on six benchmark microarray datasets show that our method can obtain higher classification accuracy using as few genes as possible, especially neighborhood mutual information can directly continuous features. Feature genes selected has an important meaning for understanding microarray data and finding pathogenic genes of cancer. It is an effective and efficient method for feature genes selection.

DNA microarray technique can detect tens of thousands of genes activity in cells and has been widely used in clinical diagnosis. However, microarray data has the characteristics of high dimension and small samples, moreover many irrelevant and redundant genes also decrease performance of classification algorithm. Feature gene selection is an effective method to solve this problem. This paper proposes a hybrid feature gene selection method. Firstly, a lot of irrelevant genes from original data were eliminated by using reliefF algorithm, and the candidate feature genes subset is obtained; Secondly, Fuzzy neighborhood rough set with information entropy which deals directly with continuous data is proposed to reduce redundant genes among genes subset above. Here, differential evolution algorithm is used to optimize radius before reduction by using fuzzy neighborhood rough set, because radius of neighborhood greatly affects reduction performance. The simulation results on six microarray datasets indicate that our method can obtain higher classification accuracy by using as few genes as possible, especially feature genes selected are important for understanding microarray data and identifying the pathogenic genes. The results demonstrated that this method is effective and efficient for feature genes selection.

The application of the cellulase gene (celA) as a selection marker of food-grade integration system wasinvestigated in Lactobacillus (Lb.) casei, Lactococcus lactis, and Leuconostoc (Leu.) mesenteroides. The 6.0-kb vector pOC13containing celA from Clostridium thermocellum with an integrase gene and a phage attachment site originating frombacteriophage A2 was used for site-specific recombination into chromosomal DNA of lactic acid bacteria (LAB). pOC13 wasalso equipped with a broad host range plus replication origin from the lactococcal plasmid pWV01, and a controllablepromoter of nisA (PnisA) for the production of foreign proteins. pOC13 was integrated successfully into Lb. casei EM116, andpOC13 integrants were easily detectable by the formation of halo zone on plates containing cellulose. Recombinant Lb. caseiEM116::pOC13 maintained these traits in the absence of selection pressure during 100 generations. pOC13 was integrated intothe chromosome of L. lactis and Leu. mesenteroides, and celA acted as an efficient selection marker. These results show thatcelA can be used as a food-grade selection marker, and that the new integrative vector could be used for the production offoreign proteins in LAB.

수천개의 Gene Expression Measurement를 생성해 내는 DNA Microarray 연구는 조직과 세포의 표본으로부터 진단에 유용한 Gene Expression 정보를 모으게 된다. 이런 종류의 Data를 분석하기 위하여 SVM(Support Vector Machine)을 사용한 새로운 방법이 연구되어왔다. 본 논문에서는 Gene Expression Data에 대한 고유벡터(Eigen Vector)를 이용하여 SVM의 성능을 향상시키고 질병진단에 유용한 Gene을 찾아 내는 알고리즘을 기술한다. 고유벡터를 통하여 Gene을 선택적으로 SVM Learning에 참가 시키고 분류의 결과를 통하여 추가된 Gene이 질병 진단에 미치는 영향력을 알아냄으로써 질병에 대한 Gene 역할을 파악 하는데 활용할 수 있다.

수천개의 Gene Expression Measurement를 생성해 내는 DNA Microarray 연구는 조직과 세포의 표본으로부터 진단에 유용한 Gene Expression 정보를 모으게 된다. 이런 종류의 Data를 분석하기 위하여 SVM(Support Vector Machine)을 사용한 새로운 방법이 연구되어왔다. 본 논문에서는 Gene Expression Data에 대한 고유벡터(Eigen Vector)를 이용하여 SVM의 성능을 향상시키고 질병진단에 유용한 Gene을 찾아 내는 알고리즘을 기술한다. 고유벡터를 통하여 Gene을 선택적으로 SVM Learning에 참가 시키고 분류의 결과를 통하여 추가된 Gene이 질병 진단에 미치는 영향력을 알아냄으로써 질병에 대한 Gene 역할을 파악 하는데 활용할 수 있다.

In this paper, we propose a heuristic method to select features using a Two-Phase Markov Blanket-based (TPMB) algorithm. The first phase, filtering phase, of TPMB algorithm works by filtering the obviously redundant features. A non-linear correlation method based on Information theory is used as a metric to measure the redundancy of a feature [1]. In second phase, approximating phase, the Markov Blanket (MB) of a system is estimated by employing the concept of cross entropy to identify the MB. We perform experiments on microarray data and report two popular dataset, AML-ALL [3] and colon tumor [4], in this paper. The experimental results show that the TPMB algorithm can significantly reduce the number of features while maintaining the accuracy of the classifiers.

In microarray technology, many diverse experimental features can cause biases including RNA sources, microarray production or different platforms, diverse sample processing and various experiment protocols. These systematic effects cause a substantial obstacle in the analysis of microarray data. When such data sets derived from different experimental processes were used, the analysis result was almost inconsistent and it is not reliable. Therefore, one of the most pressing challenges in the microarray field is how to combine data that comes from two different groups. As the novel trial to integrate two data sets with batch effect, we simply applied standardization to microarray data before the significant gene selection. In the gene selection step, we used new defined measure that considers the distance between a gene and an ideal gene as well as the between-slide and within-slide variations. Also we discussed the association of biological functions and different expression patterns in selected discriminative gene set. As a result, we could confirm that batch effect was minimized by standardization and the selected genes from the standardized data included various expression pattems and the significant biological functions.

Gene expression profiles may offer more information than morphology and provide an alternative to morphology- based tumor classification systems. Informative gene selection is finding gene subsets that are able to discriminate between tumor types, and may have clear biological interpretation. Gene selection is a fundamental issue in gene expression based tumor classification. In this report, techniques for selecting informative genes are illustrated and supervised shaving introduced as a gene selection method in the place of a clustering algorithm. The supervised shaving method showed good performance in gene selection and classification, even though it is a clustering algorithm. Almost selected genes are related to leukemia disease. The expression profiles of 3051 genes were analyzed in 27 acute lymphoblastic leukemia and 11 myeloid leukemia samples. Through these examples, the supervised shaving method has been shown to produce biologically significant genes of more than $94\%$ accuracy of classification. In this report, SVM has also been shown to be a practicable method for gene expression-based classification.

Microarray gene expression profiling technology is one of the most important research topics in clinical diagnosis of disease. Given thousands of genes, only a small number of them show strong correlation with a certain phenotype. To identify such an optimal subset from thousands of genes is intractable, which plays a crucial role when classify multiple-class genes express models from tumor samples. This paper proposes an efficient classifier design method to simultaneously select the most relevant genes using an intelligent genetic algorithm (IGA) and design an accurate classifier using Support Vector Machine (SVM). IGA with an intelligent crossover operation based on orthogonal experimental design can efficiently solve large-scale parameter optimization problems. Therefore, the parameters of SVM as well as the binary parameters for gene selection are all encoded in a chromosome to achieve simultaneous optimization of gene selection and the associated SVM for accurate tumor classification. The effectiveness of the proposed method IGA/SVM is evaluated using four benchmark datasets. It is shown by computer simulation that IGA/SVM performs better than the existing method in terms of classification accuracy.

In this paper we consider the well-known Weibull regression models for survival analysis. These models are usually used with few covariates and many observations (subjects). But, for a typical setting of gene expression data from DNA microarray, we need to consider the case where the number of covariates exceeds the number of samples . For a given vector of response values which are times to event(death or censored times) and gene expressions(covariates), we address the issue of how to reduce the dimension by selecting the significant genes. This approach enables us to estimate the survival curve when . In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional flexibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in effect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo(MCMC) method. We demonstrate the use of the methodology to diffuse large B-cell lymphoma(DLBCL) complementary DNA(cDNA) data and Breast Carcinomas data.

Comparative Statistic Module(CSM) provides more reliable list of significant genes to genomics researchers by offering the commonly selected genes and a method of choice by calculating the rank of each statistical test based on the average ranking of common genes across the five statistical methods, i.e. t-test, Kruskal-Wallis (Wilcoxon signed rank) test, SAM, two sample multiple test, and Empirical Bayesian test. This statistical analysis module is implemented in Perl, and R languages.

Gene expression data is obtained through many stages of an experiment and errors produced during the process may cause missing values. Due to the distinctness of the data so called 'small n large p', genes have to be selected for statistical analysis, like classification analysis. For this reason, imputation and gene selection are important in a microarray data analysis. In the literature, imputation, gene selection and classification analysis have been studied respectively. However, imputation, gene selection and classification analysis are sequential processing. For this aspect, we compare the performance of classification methods after imputation and gene selection methods are applied to microarray data. Numerical simulations are carried out to evaluate the classification methods that use various combinations of the imputation and gene selection methods.